Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Python Executor
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSo...
skill openclawclawhub Free
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSo...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.5
Provenance
- Publisher
- okaris
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Python Code Executor
Execute Python code in a safe, sandboxed environment with 100+ pre-installed libraries.
Quick Start
curl -fsSL https://cli.inference.sh | sh && infsh login # Run Python code infsh app run infsh/python-executor --input '{ "code": "import pandas as pd\nprint(pd.__version__)" }' Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.
App Details
PropertyValueApp IDinfsh/python-executorEnvironmentPython 3.10, CPU-onlyRAM8GB (default) / 16GB (high_memory)Timeout1-300 seconds (default: 30)
Input Schema
{ "code": "print('Hello World!')", "timeout": 30, "capture_output": true, "working_dir": null }
Web Scraping & HTTP
requests, httpx, aiohttp - HTTP clients beautifulsoup4, lxml - HTML/XML parsing selenium, playwright - Browser automation scrapy - Web scraping framework
Data Processing
numpy, pandas, scipy - Numerical computing matplotlib, seaborn, plotly - Visualization
Image Processing
pillow, opencv-python-headless - Image manipulation scikit-image, imageio - Image algorithms
Video & Audio
moviepy - Video editing av (PyAV), ffmpeg-python - Video processing pydub - Audio manipulation
3D Processing
trimesh, open3d - 3D mesh processing numpy-stl, meshio, pyvista - 3D file formats
Documents & Graphics
svgwrite, cairosvg - SVG creation reportlab, pypdf2 - PDF generation
Web Scraping
infsh app run infsh/python-executor --input '{ "code": "import requests\nfrom bs4 import BeautifulSoup\n\nresponse = requests.get(\"https://example.com\")\nsoup = BeautifulSoup(response.content, \"html.parser\")\nprint(soup.find(\"title\").text)" }'
Data Analysis with Visualization
infsh app run infsh/python-executor --input '{ "code": "import pandas as pd\nimport matplotlib.pyplot as plt\n\ndata = {\"name\": [\"Alice\", \"Bob\"], \"sales\": [100, 150]}\ndf = pd.DataFrame(data)\n\nplt.bar(df[\"name\"], df[\"sales\"])\nplt.savefig(\"outputs/chart.png\")\nprint(\"Chart saved!\")" }'
Image Processing
infsh app run infsh/python-executor --input '{ "code": "from PIL import Image\nimport numpy as np\n\n# Create gradient image\narr = np.linspace(0, 255, 256*256, dtype=np.uint8).reshape(256, 256)\nimg = Image.fromarray(arr, mode=\"L\")\nimg.save(\"outputs/gradient.png\")\nprint(\"Image created!\")" }'
Video Creation
infsh app run infsh/python-executor --input '{ "code": "from moviepy.editor import ColorClip, TextClip, CompositeVideoClip\n\nclip = ColorClip(size=(640, 480), color=(0, 100, 200), duration=3)\ntxt = TextClip(\"Hello!\", fontsize=70, color=\"white\").set_position(\"center\").set_duration(3)\nvideo = CompositeVideoClip([clip, txt])\nvideo.write_videofile(\"outputs/hello.mp4\", fps=24)\nprint(\"Video created!\")", "timeout": 120 }'
3D Model Processing
infsh app run infsh/python-executor --input '{ "code": "import trimesh\n\nsphere = trimesh.creation.icosphere(subdivisions=3, radius=1.0)\nsphere.export(\"outputs/sphere.stl\")\nprint(f\"Created sphere with {len(sphere.vertices)} vertices\")" }'
API Calls
infsh app run infsh/python-executor --input '{ "code": "import requests\nimport json\n\nresponse = requests.get(\"https://api.github.com/users/octocat\")\ndata = response.json()\nprint(json.dumps(data, indent=2))" }'
File Output
Files saved to outputs/ are automatically returned: # These files will be in the response plt.savefig('outputs/chart.png') df.to_csv('outputs/data.csv') video.write_videofile('outputs/video.mp4') mesh.export('outputs/model.stl')
Variants
# Default (8GB RAM) infsh app run infsh/python-executor --input input.json # High memory (16GB RAM) for large datasets infsh app run infsh/python-executor@high_memory --input input.json
Use Cases
Web scraping - Extract data from websites Data analysis - Process and visualize datasets Image manipulation - Resize, crop, composite images Video creation - Generate videos with text overlays 3D processing - Load, transform, export 3D models API integration - Call external APIs PDF generation - Create reports and documents Automation - Run any Python script
Important Notes
CPU-only - No GPU/ML libraries (use dedicated AI apps for that) Safe execution - Runs in isolated subprocess Non-interactive - Use plt.savefig() not plt.show() File detection - Output files are auto-detected and returned
Related Skills
# AI image generation (for ML-based images) npx skills add inference-sh/skills@ai-image-generation # AI video generation (for ML-based videos) npx skills add inference-sh/skills@ai-video-generation # LLM models (for text generation) npx skills add inference-sh/skills@llm-models
Documentation
Running Apps - How to run apps via CLI App Code - Understanding app execution Sandboxed Code Execution - Safe code execution for agents
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md